Our main aim is not to beat any particular method for fairness or domain adaption, it is rather to highlight the commonalities. From a technical side, the most related work probably is the one by Ganin et al. [1]} based on Zemel et al. [2]}. However instead of adding a new gradient reversal layer, we simply reduce an additional affinity loss during training. Hence, our method can be easily applied to any existing network architecture including classification, regression tasks or auto-encodersIn this paper we focus on the classification tasks with one categorical sensitive variable.. Experiments demonstrate that a pretrained network with fixed weights can be simply debiased by adding a fair representation layer. While many approaches struggle with unbalanced datasets both in terms of the target and the sensitive attribute [3]}, our approach is not very negatively impacted. We are able to improve fairness, interpretability and domain adaptation within one very simple approach.
